Can controlled environment chambers be used for better seed-propagated strawberry transplants?

Paper referenced:

Tsuruyama, J., & Shibuya, T. (2018). Growth and flowering responses of seed-propagated strawberry seedlings to different photoperiods in controlled environment chambers. HortTechnology, 28(4), 453–458.


Although other crops are often in the spotlight when it comes to growing food in greenhouses, strawberry is gaining popularity and for good reason. It can be grown in greenhouses through the cold winter months in temperate climates to make local, fresh, high quality fruit available when not much else is. To maximize fruit set and profitability, starting the production cycle with high quality transplants is a necessity. Transplants, also known as plug or tray plants, can be produced from seed, rooted runner tips, or field dug bare root crowns, though, use of field dug plants can introduce pests and diseases into the greenhouse. Seed propagated hybrid strawberry cultivars suited for greenhouse production have been developed, leading to increased adoption of this technique in Europe and Japan.

Controlled environment technology presents strawberry plug producers with the tools needed to provide growers with high-quality transplants due to the tremendous level of control over environmental conditions such as light quality and quantity, humidity, and temperature. This high degree of control is advantageous because greenhouse grown plugs produced for August/September transplant can experience high temperatures and variable conditions, which can delay flowering and fruit production. However, with the use of an indoor controlled environment facility, plants can be grown under optimal conditions no matter the weather outside. In addition to temperature, photoperiod (the amount of time plants are exposed to light) as well as light intensity, can affect the growth and flowering of strawberries. Due to this, determining the optimal photoperiod for indoor plug production could lead to enhanced quality of transplants.

In this study the authors use two-seed propagated cultivars, one European (‘Elan’) and one Japanese (‘Yotsuboshi’), to produce tray plants for mid-August transplant. Both cultivars are long-day strawberry types which generally meaning flowering is promoted by long light periods.

To start the experiment, once seedlings had germinated and grown two true leaves, at 23 days old, they were replanted into larger trays for the light treatment phase. Next, groups of seedlings from both cultivars were subjected to different propagation systems. Four groups were grown in a growth chamber with blue/red LED lighting, this allowed the researchers to control the conditions the plants experienced, while the control group was grown in a greenhouse. The growth chambers were maintained at 25°C (77°F), but had different photoperiods and light intensities. The photoperiods tested in growth chambers were 8, 12, 16, and 24 hours. To ensure all plants received the same total amount of light, the light intensities were proportionally adjusted based on photoperiod, so the shortest photoperiod had the highest intensity and the longest had the lowest. The control plants were subjected to summer greenhouse conditions, moderated by shading during the day and air conditioning at night. The greenhouse average photoperiod was 13.6 hours with day temperatures around 30°C and night temperatures around 23°C for an average temperature of 26.8°C (80.2°F). All plants were grown in their respective treatment conditions for 38 days. After which, 10 plants per cultivar of each treatment were measured for dry mass, leaf area, leaf number, and length of the longest petiole to assess plant growth. Using pre- and post-treatment leaf area and dry mass, the relative growth rate (increase in mass), net assimilation rate (photosynthesis efficiency), and leaf area ratio were calculated.

After 38 days, when they had 6-7 leaves, plants were transplanted into a different greenhouse for the flower emergence trial, which lasted 110 days (from mid-August to late November). Plants were checked daily for flower bud emergence. Temperatures started high around 40°C (104°F) in August but slowly cooled as time progressed to a more typical strawberry production temperature range (25-10°C).

Results and Discussion
In both cultivars long-day, low intensity lighting out performed short-day and greenhouse conditions regarding plant mass, leaf area, petiole length, relative growth rate, and net assimilation rate, indicating enhanced photosynthetic efficiency. This suggests that plants were able to use the steady low amount of light over long periods more efficiently than high amounts of light over short periods or the summer greenhouse conditions, which exceeded the ideal growing temperatures for strawberry. Thus suggesting that using a controlled environment system with low intensity long-day lighting was more effective for plant growth than the greenhouse control.
Regarding how long it took plants to flower once transplanted into a fruit production greenhouse, for Elan, long-day conditions nearly halved time to flower compared to greenhouse control and short day photoperiods. This suggests that the long-day low intensity light treatments were effective for inducing flowers earlier than the summer greenhouse or short day conditions.
In Yotsuboshi however, photoperiod treatments did not have an effect on time to flower. Yet, the greenhouse control flowered slightly sooner than the photoperiod treatments, which may be due to transplant shock that the controlled environment plants experienced. These results demonstrate what other studies have found in that cultivars can react differently to the same conditions even if they are the same photoperiod type. Thus, these results suggest that more research is needed into which factors and their levels affect Yotsuboshi flowering to better understand the cultivar’s flower emergence.
Overall this study demonstrates that using low intensity LED lighting in controlled environment settings for long-day seed-propagated strawberry tray plants is a viable alternative to summer greenhouse production.

Remote Control of Greenhouse Vegetable Production with Artificial Intelligence—Greenhouse Climate, Irrigation, and Crop Production


Hemming, S., de Zwart, F., Elings, A., Righini, I., & Petropoulou, A. (2019). Remote Control of Greenhouse Vegetable Production with Artificial Intelligence—Greenhouse Climate, Irrigation, and Crop Production. Sensors, 19(8), 1807. 


As the global population keeps growing, so does the demand for healthy, fresh, and accessible food. Greenhouse environments contribute an important function in providing fresh food at a high production rate while maximizing resource efficiency. The greenhouse industry has stumbled over the obstacle of finding enough skilled staff to manage these crop production systems. Modern high-tech greenhouses are equipped with process computers, and, to add more automated control, greenhouse climate and crop models have been developed. The use of Artificial Intelligence (AI) has reached breakthroughs in many areas and has not been used yet to control climate and irrigation and make crop management decisions for growing a greenhouse crop autonomously. An international challenge was conducted on autonomous greenhouses in 2018 at the high-tech research greenhouses of Wageningen University and Research in cooperation with five multi-disciplinary international teams to combine the use of modern AI algorithms and the greenhouse crop production of cucumbers cultivar “Hi-Power”. This article aims to describe the results of the teams in terms of optimizing crop yields and net profit using state-of-the-art AI algorithms for cucumber production.


The experiments were conducted in six identical greenhouse compartments equipped with standard motor movers. Cucumber seedlings were grown in rockwool substrate cubes and placed on slabs on hanging gutters. The five participating teams were: Sonoma, iGrow, deep_greens, The Croperators, and AiCu. These teams were able to remotely control the motors’ activity in their greenhouse compartment by using their own original AI algorithm, varying in design and techniques. As part of the competition, a sixth team led by Dutch growers controlled a greenhouse compartment and served as a reference. The competing teams used their original AI algorithms to regulate the climate and irrigation setpoints through a central computer and operated in the greenhouse compartments accordingly. Standard sensors set up in the greenhouse compartments continuously measured data and calculations were made based on these and digitally sent back to the teams. Teams were also allowed to install additional sensors in their compartments at the start of the experiment. Three harvest quality parameters were established: A: no defects (>375g), B: defects in shape, color, or others (300-374g), and C: less than 300g per fruit. Harvest data was measured manually by greenhouse staff and digitally sent to the teams.

The teams were judged based on three criteria: Sustainability, based on resource use efficiency (20%), Net profit, based on the number of fruits harvested per price of the fruit and category (50%), and AI algorithm, based on originality and efficacy of the algorithm (30%). Two models were used to analyze and compare the different AI algorithm approaches since the operation resulted in differences in cropping, climate, irrigation strategies, harvest yields, and resource use efficiencies. However, the combined model did not represent the presence and effects of pests and disease. The combined model was carried out to compare the calculated output as the predicted fresh cucumber yield per greenhouse compartment versus the realized cucumber yield in the same greenhouse compartment to verify the models. Additionally, model calculations were carried out applying the cropping strategy, lighting strategy, and climate strategy of other teams for each greenhouse compartment to predict the changes in yield and compare these.

Figure 2 – Scheme of data exchange (Hemming et al., 2019)

Results and Discussion

Out of all the teams, including the Dutch growers, the Sonoma team resulted in the highest production of cucumbers and consistently did this throughout the experiment. They predicted that by having a high daily light integral, they would be able to achieve a greater harvest, as they did, focusing their algorithms on this. Other teams decided to increase daily light integral and maintain a low carbon dioxide concentration (The Croperators) or have lower daily light integrals in the beginning to minimize fruit abortion (Dutch growers) which resulted in lower yields but led to a greater understanding of important production factors. All teams began the experiment with a relatively low amount of carbon dioxide, where most teams increased their concentration throughout the experiment and later diminished their dosage towards the end, the Sonoma team decided to increase their carbon dioxide concentration continuously throughout the total cropping period.

The goal of maximizing crop management is finding the optimum combination of the climate, lighting, and cropping strategies. This can be achieved by attaining the greatest number of fruits per area without hindering photosynthesis. Light, for example, is the foundation for healthy growth. For this particular experiment, having higher light integrals resulted in higher yields for the teams. As predicted with the combined models, the teams iGrow, AiCu, and growers could have possibly achieved a higher yield if they would have used the lighting strategies of either team Sonoma or The Croperators. Overall, most of the teams were able to obtain a decent production using a low amount of resources while reaching a net profit close to or better than manual commercial growers.

The goal of this experiment was to see how AI algorithms would become better at making crop management decisions and their effects on the greenhouse climate and production. At the end of the cucumber crop’s growth period, all the algorithms developed for this experiment resulted in establishing a successful greenhouse environment. As this was the first successful experiment on remotely controlling a greenhouse for cucumber production using artificial intelligence algorithms, the authors demonstrated that AI algorithms can compete, and even outperform, experienced manual growers. Artificially assisted or managed greenhouse systems could be a useful tool to grow crops where there is limited knowledge. In the future, more developments and studies in this area will be needed to make AI an alternative for trained and skilled greenhouse workers and growers, which to this day, greenhouses cannot function without.

Blue Radiation Interacts with Green Radiation to Influence Growth and Predominantly Controls Quality Attributes of Lettuce


Meng, Q., J. Boldt, and E.S. Runkle. 2020. Blue radiation interacts with green radiation to influence growth and predominately controls quality attributes of lettuce. Journal of the American Society for Horticultural Sciences 145(2):75-87.

With recent shifts in modern agriculture to more urban environments, indoor farming has become increasingly popular. Such an environment allows growers to control everything from air flow to water. Arguably the most important aspect that can be controlled is lighting, specifically with the use of light-emitting diodes (LEDs) that allow for customizable wavelengths for varying stages in a plant’s life cycle. Research has shown that different wavelengths can produce different results. For instance, exposing certain varieties of lettuce to blue radiation (400-500 nm) has been tied to a significant reduction in biomass weight, as well as an increase in the production of secondary metabolites. Growers will often combine blue with red and far-red radiation (600-800 nm) to achieve desired results. Green radiation (500-600 nm), however, does not have much of a history of being used by growers, despite its ability to penetrate deep into the leaves. This has huge implications on its ability to drive photosynthesis and has recently been studied as a substitute for blue radiation. Previous research on this subject has shown an increase in biomass of several lettuce varieties, but the authors believe this could have been attributed to the shifting levels of blue radiation. To combat this, they designed a new experiment to keep levels of blue radiation constant and substitute red radiation for green.


In this experiment, researchers focused on ‘Rouxai’ red leaf lettuce and tested the effects of varying wavelengths using LED lighting. During the light quality treatment phase of the experiment, each treatment had a 20 hour photoperiod and a total photosynthetic photon flux density (PPFD) of 180 μmol m-2 s-1 . They exposed the lettuce to nine different treatments of lighting, including combinations of blue and red radiation, as well as introducing green radiation at a photon flux density (PFD) of 60 μmol m-2 s-1 in place of a reduction in red radiation. Researchers measured biomass accumulation including fresh and dry mass, different morphological features such as plant diameter and leaf number, and coloration of the foliage. They found that at 20 μmol m-2 s-1 of blue radiation, the presence of 60 μmol m-2 s-1 green radiation increased the fresh mass of lettuce, but had negative effects on the weight at any higher levels of blue radiation. Additionally, an increase in blue radiation with 60 μmol m-2 s-1 of green radiation decreased leaf diameter, width, and length. With an increase in blue radiation, lettuce is known to have an increase in red color of the foliage. Without green radiation, the foliage became saturated at 20 μmol m-2 s-1 of blue radiation. However, with 60 μmol m-2 s-1 of green radiation, the saturation point increased to 60 μmol m-2 s-1 of blue radiation. This has interesting implications for growers wishing to increase the coloration of their foliage.


This study provides valuable information regarding the role green radiation may have in a plant’s life cycle. With an increase in fresh mass at low levels of blue radiation, incorporating green radiation at the right stages could potentially increase yield for growers. Additionally, the presence of green light has been shown to vary depending on the species and age of the crop, which implies further research is needed on the subject. Having this research and knowledge that green radiation influences photosynthesis and other varying characteristics of lettuce growth is critical for growers looking to optimize their lighting in controlled environments.